classdef DOC7 < PROBLEM
% <problem> <DOC>
% Benchmark MOP with constraints in both decision and objective spaces

%------------------------------- Reference --------------------------------
% Z. Liu and Y. Wang, Handling constrained multiobjective optimization
% problems with constraints in both the decision and objective spaces. IEEE
% Transactions on Evolutionary Computation, 2019.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2018-2019 BIMK Group. You are free to use the PlatEMO for
% research purposes. All publications which use this platform or any code
% in the platform should acknowledge the use of "PlatEMO" and reference "Ye
% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform
% for evolutionary multi-objective optimization [educational forum], IEEE
% Computational Intelligence Magazine, 2017, 12(4): 73-87".
%--------------------------------------------------------------------------

    methods
        %% Initialization
        function obj = DOC7()
            obj.Global.M = 2;
            obj.Global.D = 11;
            obj.Global.lower    = [0 zeros(1, 10)];
            obj.Global.upper    = [ 1 10 * ones(1, 10)];
            obj.Global.encoding = 'real';
        end
        %% Calculate objective values
        function PopObj = CalObj(~,X)
            [popsize,~]  = size(X);
            % basic multi-objective problem
            c1 = [-6.089 -17.164 -34.054 -5.914 -24.721 -14.986 -24.1 -10.708 -26.662 -22.179];
            g_temp = sum(X(:,2:11).* (repmat(c1, popsize, 1) + log(1E-30 + X(:,2:11)./repmat(1E-30 + sum(X(:,2:11), 2), 1, 10))), 2);
            g = g_temp +47.7648884595 +1;
            PopObj(:,1) = X(:,1);
            PopObj(:,2) = g.*(1-sqrt(PopObj(:,1))./g);
            
        end
        %% Calculate constraint violations
        function PopCon = CalCon(obj,X)
            PopObj = obj.CalObj(X);
            % constraints in objective space
            c(:,1) = max( -(PopObj(:,1) + PopObj(:,2)-1), 0);
            c(:,2) = max(-(PopObj(:,1) - 0.5).*( PopObj(:,1)+ PopObj(:,2) - 1 - abs(sin(10*pi*(PopObj(:,1) - PopObj(:,2) + 1) ))  ), 0);
            c(:,3) = max(- ( abs(- PopObj(:,1) + PopObj(:,2))./sqrt(2) - 0.1./sqrt(2)), 0);
    
            % constraints in decision space
            c(:,4) = abs(X(:, 2) + 2 * X(:, 3) + 2 * X(:, 4) + X(:, 7) + X(:, 11) - 2) - 0.0001;
            c(:,5) = abs(X(:, 5) + 2 * X(:, 6) + X(:, 7) + X(:, 8) - 1) - 0.0001;
            c(:,6) = abs(X(:, 4) + X(:, 8) + X(:, 9) + 2 * X(:, 10) + X(:, 11) - 1) - 0.0001;
            PopCon=c;  
        end
        %% Sample reference points on Pareto front
        function P = PF(obj,N)
            P(:,1) = [(0:0.45/(N-1):0.45),(11:20)/20]';
            P(:,2) = 1 - P(:,1);
        end
    end
end